DNA methylation (DNAm) is a crucial component of the epigenetic regulation of gene expression, orchestrating tissue differentiation and development from fetus to adolescence, and likely beyond. In the human brain, gene expression is extremely dynamic through this important time frame of development (Colantuoni 2011) and dysregulation of the early maturational stages of the brain plays a central role in neurodevelopmental disorders such as schizophrenia (Weinberger 2011). We hypothesize that deviations from essential DNAm developmental trajectories, either through genetic factors and/or environmental insults, could interfere with these carefully coordinated patterns of gene expression. Since schizophrenia risk established by epigenetic mechanisms must pre-date the development of the disorder, and the majority of the variability in site-specific DNAm occurs during fetal life through young adulthood, we propose whole genome bisulfite sequencing (WGBS) in post-mortem brain tissue from normal individuals ranging from the late first trimester through 25 years of age. This next-generation sequencing approach on 60 brain samples can measure DNAm levels at every cytosine dinucleotide (including non-CpG contexts) in each genome, whereas existing DNAm microarray technologies only measure DNAm levels at a fraction of the total CpGs. This magnified resolution permits identifying regions where DNAm levels at adjacent cytosines are associated with development and aging (differentially methylated regions, or DMRs). We can then integrate these DMRs with existing RNA sequencing data on the same brain samples to identify which DMRs are functionally involved in regulating gene expression in the developing human brain. The lack of knowledge about the normal patterns of DNA methylation during the first three decades of life is a significant hindrance towards understanding how genetic variation interacts with environmental factors in altering risk for illness. This integrative approach, combining whole-genome bisulfite sequencing data and RNA sequencing data, prioritizes regions of epigenome imperative to development, defining multidimensional patterns of normal that are likely go awry in schizophrenia.